Analyzing network data in biology and medicine : an interdisciplinary textbook for biological, medical and computational scientists
AUTHOR : –
CALL NO : W26.5 A532 2019
IMPRINT : Cambridge : Cambridge University Press, c2019
The increased and widespread availability of large network data resources in recent years has resulted in a growing need for effective methods for their analysis. The challenge is to detect patterns that provide a better understanding of the data. However, this is not a straightforward task because of the size of the data sets and the computer power required for the analysis. The solution is to devise methods for approximately answering the questions posed, and these methods will vary depending on the data sets under scrutiny. This cutting-edge text introduces biological concepts and biotechnologies producing the data, graph and network theory, cluster analysis and machine learning, before discussing the thought processes and creativity involved in the analysis of large-scale biological and medical data sets, using a wide range of real-life examples. Bringing together leading experts, this text provides an ideal introduction to and insight into the interdisciplinary field of network data analysis in biomedicine.
- Introduces graph and network theory, as well as some commonly used machine learning methods and their applications to analyze complex heterogeneous data sets
- Covers a wide array of topics from using personalized genetic tests to predicting disease risks, analysis of epigenetic and disease data, as well as protein interaction and -omics data
- Includes examples from current hot topics, such as network neuroscience and network medicine
